The problem of finding pathological features artificially from medical images over time, which ultimately leads to deterioration, has increased significantly. Thus, it has become a crucial area of interest for researchers. This study introduced a secondary feature extraction method (ω‍-net) with the ability to segment lung, liver, nucleus, and brain tumors. First, we used the full-size Unet as the primary feature extraction path. Similarly, we used the third layer on the upsampling path as the starting layer to expand the secondary feature extraction path to enhance the feature extraction capability. Second, we introduced two new-attention mechanisms at various stages for targeted optimization to establish long-term channel dependence and enhance feature location information. Finally, the study reproduced 10 classic networks. With the application of Unet in the medical imaging field, the commonly used indicators, including mean intersection of union, sensitiveness, precision, and accuracy of the proposed network, increase by 0.0787, 0.1287, 0.1216, and 0.0201, respectively, compared with the benchmark network. The study evaluated the effectiveness and superiority of the introduced network and compared the index values and visualization results on four types of datasets. The results showed that the introduced network outperformed the other existing networks.